Adjustment set
An adjustment set is a set of variables conditioned on to block noncausal paths between a treatment and outcome while preserving the causal paths.
Causal Inference for Data Science
An adjustment set is a set of variables conditioned on to block noncausal paths while preserving the causal paths between a treatment and an outcome.
- How it is used
- Guides variable selection before applying the adjustment formula to estimate a causal effect.
- How deep it goes
- Explains path-based selection, collider risks, bias equivalence, and variance trade-offs, but defers a definitive theory of efficient sets.
- Examples
- Uses sports injury diagrams, shoe pricing with missing socioeconomic context, control-variable tables, and R and Python regression simulations.
- What you can do after
- Apply the back-door criterion and Dagitty to identify candidate sets before estimating causal effects.
Read how Causal Inference for Data Science explains adjustment set →
Causal AI
An adjustment set is a set of variables that satisfies the backdoor criterion for estimating a causal effect between specified variables.
- How it is used
- Selects variables to block backdoor paths when identifying a causal effect from observational data.
- How deep it goes
- Defines the graphical criterion, derives its adjustment formula, and carries the selected set into estimation and robustness checks.
- Examples
- Uses online gaming DAGs and a DoWhy purchase-effect workflow with guild membership, playing time, and player skill.
- What you can do after
- Apply it via DoWhy to identify a backdoor estimand, choose controls, and test sensitivity to omitted confounders.
Related concepts
- backdoor identificationused by · Backdoor identification is the identification of a causal effect by adjusting for variables that block noncausal backdoor paths between an exposure and an outcome.
- backdoor criterionuses · The backdoor criterion is a graphical condition for identifying a causal effect by requiring a variable set to block every backdoor path while excluding treatment descendants.
- average treatment effect estimationuses · Average treatment effect estimation quantifies the population-average difference in outcomes that would result from two alternative treatments or interventions.
- estimandaddresses · An estimand is the population quantity or probabilistic target that a statistical analysis seeks to determine.
- total effectuses · A total effect is the overall causal effect of a treatment or variable on an outcome, including all direct and mediated pathways.
- backdoor pathaddresses · A backdoor path is a path between a putative cause and an outcome that begins with an arrow into the cause and can create a noncausal association.
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